{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting Started\n",
    "> Fit an LSTM and NHITS model"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook provides an example on how to start using the main functionalities of the NeuralForecast library. The `NeuralForecast` class allows users to easily interact with `NeuralForecast.models` PyTorch models. In this example we will forecast AirPassengers data with a classic `LSTM` and the recent `NHITS` models. The full list of available models is available [here](https://nixtla.github.io/neuralforecast/models.html).\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can run these experiments using GPU with Google Colab.\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/Nixtla/neuralforecast/blob/main/nbs/examples/Getting_Started.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Installing NeuralForecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "!pip install neuralforecast"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Loading AirPassengers Data\n",
    "\n",
    "The `core.NeuralForecast` class contains shared, `fit`, `predict` and other methods that take as inputs pandas DataFrames with columns `['unique_id', 'ds', 'y']`, where `unique_id` identifies individual time series from the dataset, `ds` is the date, and `y` is the target variable. \n",
    "\n",
    "In this example dataset consists of a set of a single series, but you can easily fit your model to larger datasets in long format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1949-01-31</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1949-02-28</td>\n",
       "      <td>118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1949-03-31</td>\n",
       "      <td>132.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1949-04-30</td>\n",
       "      <td>129.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1949-05-31</td>\n",
       "      <td>121.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   unique_id         ds      y\n",
       "0        1.0 1949-01-31  112.0\n",
       "1        1.0 1949-02-28  118.0\n",
       "2        1.0 1949-03-31  132.0\n",
       "3        1.0 1949-04-30  129.0\n",
       "4        1.0 1949-05-31  121.0"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from neuralforecast.utils import AirPassengersDF\n",
    "\n",
    "Y_df = AirPassengersDF # Defined in neuralforecast.utils\n",
    "Y_df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{.callout-important}\n",
    "DataFrames must include all `['unique_id', 'ds', 'y']` columns.\n",
    "Make sure `y` column does not have missing or non-numeric values. \n",
    ":::"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Model Training"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit the models"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `NeuralForecast.fit` method you can train a set of models to your dataset. You can define the forecasting `horizon` (12 in this example), and modify the hyperparameters of the model. For example, for the `LSTM` we changed the default hidden size for both encoder and decoders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from neuralforecast import NeuralForecast\n",
    "from neuralforecast.models import LSTM, NHITS, RNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "horizon = 12\n",
    "\n",
    "# Try different hyperparmeters to improve accuracy.\n",
    "models = [LSTM(h=horizon,                    # Forecast horizon\n",
    "               max_steps=500,                # Number of steps to train\n",
    "               scaler_type='standard',       # Type of scaler to normalize data\n",
    "               encoder_hidden_size=64,       # Defines the size of the hidden state of the LSTM\n",
    "               decoder_hidden_size=64,),     # Defines the number of hidden units of each layer of the MLP decoder\n",
    "          NHITS(h=horizon,                   # Forecast horizon\n",
    "                input_size=2 * horizon,      # Length of input sequence\n",
    "                max_steps=100,               # Number of steps to train\n",
    "                n_freq_downsample=[2, 1, 1]) # Downsampling factors for each stack output\n",
    "          ]\n",
    "nf = NeuralForecast(models=models, freq='M')\n",
    "nf.fit(df=Y_df)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{.callout-tip}\n",
    "The performance of Deep Learning models can be very sensitive to the choice of hyperparameters. Tuning the correct hyperparameters is an important step to obtain the best forecasts. The `Auto` version of these models, `AutoLSTM` and `AutoNHITS`, already perform hyperparameter selection automatically.\n",
    ":::"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict using the fitted models\n",
    "\n",
    "Using the `NeuralForecast.predict` method you can obtain the `h` forecasts after the training data `Y_df`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 50.58it/s]\n",
      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 126.52it/s]\n"
     ]
    }
   ],
   "source": [
    "Y_hat_df = nf.predict()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `NeuralForecast.predict` method returns a DataFrame with the forecasts for each `unique_id`, `ds`, and model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>LSTM</th>\n",
       "      <th>NHITS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1961-01-31</td>\n",
       "      <td>424.380310</td>\n",
       "      <td>453.039185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1961-02-28</td>\n",
       "      <td>442.092010</td>\n",
       "      <td>429.609192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1961-03-31</td>\n",
       "      <td>448.555664</td>\n",
       "      <td>498.796204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1961-04-30</td>\n",
       "      <td>473.586609</td>\n",
       "      <td>509.536224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1961-05-31</td>\n",
       "      <td>512.466370</td>\n",
       "      <td>524.131592</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   unique_id         ds        LSTM       NHITS\n",
       "0        1.0 1961-01-31  424.380310  453.039185\n",
       "1        1.0 1961-02-28  442.092010  429.609192\n",
       "2        1.0 1961-03-31  448.555664  498.796204\n",
       "3        1.0 1961-04-30  473.586609  509.536224\n",
       "4        1.0 1961-05-31  512.466370  524.131592"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_hat_df = Y_hat_df.reset_index()\n",
    "Y_hat_df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Plot Predictions"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we plot the forecasts of both models againts the real values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize = (20, 7))\n",
    "plot_df = pd.concat([Y_df, Y_hat_df]).set_index('ds') # Concatenate the train and forecast dataframes\n",
    "plot_df[['y', 'LSTM', 'NHITS']].plot(ax=ax, linewidth=2)\n",
    "\n",
    "ax.set_title('AirPassengers Forecast', fontsize=22)\n",
    "ax.set_ylabel('Monthly Passengers', fontsize=20)\n",
    "ax.set_xlabel('Timestamp [t]', fontsize=20)\n",
    "ax.legend(prop={'size': 15})\n",
    "ax.grid()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{.callout-tip}\n",
    "For this guide we are using a simple `LSTM` model. More recent models, such as `RNN`, `GRU`, and `DilatedRNN` achieve better accuracy than `LSTM` in most settings. The full list of available models is available [here](https://nixtla.github.io/neuralforecast/models.html).\n",
    ":::"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "- [Boris N. Oreshkin, Dmitri Carpov, Nicolas Chapados, Yoshua Bengio (2020). \"N-BEATS: Neural basis expansion analysis for interpretable time series forecasting\". International Conference on Learning Representations.](https://arxiv.org/abs/1905.10437)<br>\n",
    "- [Cristian Challu, Kin G. Olivares, Boris N. Oreshkin, Federico Garza, Max Mergenthaler-Canseco, Artur Dubrawski (2021). NHITS: Neural Hierarchical Interpolation for Time Series Forecasting. Accepted at AAAI 2023.](https://arxiv.org/abs/2201.12886)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
